Abstract
The problem of navy personnel incurring in disciplinary actions has major consequences in the productivity and motivation of these individuals for getting the job done. Leaving it unsolved may negatively affect their careers, working environments, peers, families and in some cases the Navy’s reputation. The implementation of data mining is widely considered as a powerful instrument for acquiring new knowledge from a pile of historical data, which is normally left unstudied. The main purpose of this paper is to use a data-driven approach to generate a descriptive model aimed at discovering knowledge, insights and interesting patterns in the personnel misconduct. The results reveal promising insights, hence the reliability of this work as a decision making and decision support tool.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
MacGregor, A.J., Tang, J.J., Dougherty, A.L., Galarneau, M.R.: Deployment-related injury and posttraumatic stress disorder in US military personnel. Injury 44(11), 1458–1464 (2013)
Thandi, G., et al.: Alcohol misuse in the United Kingdom Armed Forces: a longitudinal study. Drug Alcohol Depend. 156, 78–83 (2015)
Campbell, J.P.: An overview of the army selection and classification project (project A). Pers. Psychol. 43(2), 231–239 (1990)
Rosellini, A.J., et al.: Using administrative data to identify US Army soldiers at high-risk of perpetrating minor violent crimes. J. Psychiatr. Res. 84, 128–136 (2017)
Fine, S., Goldenberg, J., Noam, Y.: Integrity testing and the prediction of counterproductive behaviours in the military. J. Occup. Organ. Psychol. 89(1), 198–218 (2016)
Ameri, S., Fard, M.J., Chinnam, R.B., Reddy, C.K.: Survival analysis based framework for early prediction of student dropouts. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, New York, NY, USA, pp. 903–912 (2016)
Iam-On, N., Boongoen, T.: Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings. Int. J. Mach. Learn. Cybern. 8(2), 497–510 (2017)
Iam-On, N., Boongoen, T.: Generating descriptive model for student dropout: a review of clustering approach. Hum. Centric Comput. Inf. Sci. 7(1), 1 (2017)
Raju, D., Schumacker, R.: Exploring student characteristics of retention that lead to graduation in higher education using data mining models. J. Coll. Stud. Retent. Res. Theory Pract. 16(4), 563–591 (2015)
Kabakchieva, D.: Predicting student performance by using data mining methods for classification. Cybern. Inf. Technol. 13(1), 61–72 (2013)
Sweeney, M., Lester, J., Rangwala, H.: Next-term student grade prediction. In: IEEE International Conference on Big Data (Big Data 2015), pp. 970–975 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Mendieta, M.V., Cobeña, G. (2018). Generating a Descriptive Model to Identify Military Personnel Incurring in Disciplinary Actions: A Case Study in the Ecuadorean Navy. In: Rocha, Á., Guarda, T. (eds) Developments and Advances in Defense and Security. MICRADS 2018. Smart Innovation, Systems and Technologies, vol 94. Springer, Cham. https://doi.org/10.1007/978-3-319-78605-6_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-78605-6_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78604-9
Online ISBN: 978-3-319-78605-6
eBook Packages: EngineeringEngineering (R0)